Can We Predict Your Performance? Assessing the Relationship of Admissions Data to Academic Performance in Gross Anatomy of First-Year Medical Students 

Authors

  • Ashley N. Walker Universtiy of Florida
  • Phuong Huynh University of Florida
  • Kyle Rarey University of Florida
  • Nancy Adams University of Florida

DOI:

https://doi.org/10.62798/FJGO3997

Keywords:

Regression, gross anatomy, pre-admission, medical students

Abstract

Introduction and Objective: Educational data mining and predictive analytics in medical education have been justified to assist admissions committees and to help identify at-risk students for purposeful interventions. This study's purpose is to see if medical school entry metrics could predict first semester anatomy performance. Methods: Block entry multiple regression analysis was used with pre-admissions data from one cohort of 133 students on their anatomy lab practical scores. Results: The results showed that Cumulative Science GPA and MCAT scores are each positive, statistically significant predictors of anatomy performance, while first-generation status are significant negative predictors of academic performance on the lab practicals. Significance and Implications: The long-term goal is to utilize the formulated regression model to encourage practitioners within medical education to consider programs and activities that assist in student development of at-risk students. 

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Published

2024-09-24

How to Cite

Walker, A. N., Huynh, P., Rarey, K., & Adams, N. (2024). Can We Predict Your Performance? Assessing the Relationship of Admissions Data to Academic Performance in Gross Anatomy of First-Year Medical Students  . Florida Journal of Educational Research, 61(3), 101–110. https://doi.org/10.62798/FJGO3997

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